Evolution, development and learning with predictor neural networks

Konstantin Lakhman and Mikhail Burtsev

Abstract (Excerpt)

Study of mechanisms, that can make possible effective learning
of artificial systems in complex environments, is one of
the key issues in the adaptive systems research. In this paper
we make an attempt to implement and test a number of
ideas motivated by brain theory. Proposed model integrates
evolutionary, developmental and learning phases. The main
concept of this paper is the notion of predictor neural network
which provide distributed evaluation of the effectiveness
of goal-directed behavior on the neuronal level. We also
propose learning mechanism based on gradually inclusion of
new neuronal functional groups in case when the existing behavior
fails to deliver adaptive result. We performed basic
computational study of the model to investigate some of its’
core properties such as evolution of innate and learned behavior
and dynamics of the learning process.